Energy Trapping in Dications

The energy interaction curves of a number of diatomic and polyatomic dication systems were calculated in order to study their energy-trapping properties. Generally, the ab initio complete active space multiconfiguration self-consistent field method was used in an extended valence + polarization basis set, with compact effective potentials replacing the core electrons. The diatomic dications include all ten possible binary combinations of oxygen, sulphur, selenium, and tellurium. O₂²⁺ shows the largest exothermicity, measured from equilibrium to the monocation combination asymptote, and highest barrier to dissociation. The calculated equilibrium bond length and harmonic vibrational frequency agree very well with experiment. The O₂²⁺, SO²⁺, SeO²⁺, and TeO²⁺ series show progressively decreasing exothermicities but similar barrier heights. The non-oxides, in contrast, show similar exothermicities but decreasing barriers with increasing size of the atom constituents. These trends are interpreted in terms of both valence bond curve-crossing and molecular orbital bonding models. The ozone dication, O₃²⁺, is found to have a number of low-lying singlet and triplet stationary state structures spanning near-linear to D_3h2+ symmetries. Although the calculated exothermicity is even larger than for O₂²⁺, the barrier to O₂⁺ + O⁺ dissociation is predicted to be low in each case. O₂²⁺ surrounded by six argon atoms to model an isolating environment shows increased equilibrium O–O bond length, decreased exothermicity, and increased barrier to dissociation, relative to the bare dication. O₂²⁺ flanked at each end by a perpendicularly oriented H₂ molecule in a staggered conformation is obstructed from direct conversion to the water dimer dication by a high barrier. However, [(H₂O)₂]²⁺ dissociates smoothly from equilibrium to two water monocations with a large exothermicity but a small barrier.     

Estimating frequency of occurrence.

Various models have been proposed that attribute judgments of frequency of occurrence either to the direct coding of frequency information or to the estimation of frequency from characteristics of memory traces that are not direct frequency codes. We present three experiments that replicate and further explore a phenomenon that is significant in the distinction between direct and indirect models of frequency coding. The phenomenon is that deeper processing of stimulus material results in superior judgments of frequency for that material than does shallower processing. This effect is at least partly attributable to the number of associations that are generated by deep analysis. The results of the present experiments thereby implicate an indirect coding mechanism underlying frequency judgments. However, we also show that there is ample reason to suppose a contribution of a direct coding mechanism as well. The most reasonable view, therefore, is that multiple mechanisms are involved in the registration of frequency of occurrence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Multiplicative Operator Splittings in Nonlinear Diffusion: From Spatial Splitting to Multiple Timesteps

Operator splitting is a powerful concept used in many diversed fields of applied mathematics for the design of effective numerical schemes. Following the success of the additive operator splitting (AOS) in performing an efficient nonlinear diffusion filtering on digital images, we analyze the possibility of using multiplicative operator splittings to process images from different perspectives.We start by examining the potential of using fractional step methods to design a multiplicative operator splitting as an alternative to AOS schemes. By means of a Strang splitting, we attempt to use numerical schemes that are known to be more accurate in linear diffusion processes and apply them on images. Initially we implement the Crank-Nicolson and DuFort-Frankel schemes to diffuse noisy signals in one dimension and devise a simple extrapolation that enables the Crank-Nicolson to be used with high accuracy on these signals. We then combine the Crank-Nicolson in 1D with various multiplicative operator splittings to process images. Based on these ideas we obtain some interesting results. However, from the practical standpoint, due to the computational expenses associated with these schemes and the questionable benefits in applying them to perform nonlinear diffusion filtering when using long timesteps, we conclude that AOS schemes are simple and efficient compared to these alternatives.We then examine the potential utility of using multiple timestep methods combined with AOS schemes, as means to expedite the diffusion process. These methods were developed for molecular dynamics applications and are used efficiently in biomolecular simulations. The idea is to split the forces exerted on atoms into different classes according to their behavior in time, and assign longer timesteps to nonlocal, slowly-varying forces such as the Coulomb and van der Waals interactions, whereas the local forces like bond and angle are treated with smaller timesteps. Multiple timestep integrators can be derived from the Trotter factorization, a decomposition that bears a strong resemblance to a Strang splitting. Both formulations decompose the time propagator into trilateral products to construct multiplicative operator splittings which are second order in time, with the possibility of extending the factorization to higher order expansions. While a Strang splitting is a decomposition across spatial dimensions, where each dimension is subsequently treated with a fractional step, the multiple timestep method is a decomposition across scales. Thus, multiple timestep methods are a realization of the multiplicative operator splitting idea. For certain nonlinear diffusion coefficients with favorable properties, we show that a simple multiple timestep method can improve the diffusion process.     

THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT

Most research on learning to identify sentiment ignores "neutral" examples, learning only from examples of significant (positive or negative) polarity. We show that it is crucial to use neutral examples in learning polarity for a variety of reasons. Learning from negative and positive examples alone will not permit accurate classification of neutral examples. Moreover, the use of neutral training examples in learning facilitates better distinction between positive and negative examples.     

Synthesis with rational environments

Synthesis is the automated construction of a system from its specification. The system has to satisfy its specification in all possible environments. The environment often consists of agents that have objectives of their own. Thus, it makes sense to soften the universal quantification on the behavior of the environment and take the objectives of its underlying agents into an account. Fisman et al. introduced rational synthesis: the problem of synthesis in the context of rational agents. The input to the problem consists of temporal logic formulas specifying the objectives of the system and the agents that constitute the environment, and a solution concept (e.g., Nash equilibrium). The output is a profile of strategies, for the system and the agents, such that the objective of the system is satisfied in the computation that is the outcome of the strategies, and the profile is stable according to the solution concept; that is, the agents that constitute the environment have no incentive to deviate from the strategies suggested to them. In this paper we continue to study rational synthesis. First, we suggest an alternative definition to rational synthesis, in which the agents are rational but not cooperative. We call such problem strong rational synthesis. In the strong rational synthesis setting, one cannot assume that the agents that constitute the environment take into account the strategies suggested to them. Accordingly, the output is a strategy for the system only, and the objective of the system has to be satisfied in all the compositions that are the outcome of a stable profile in which the system follows this strategy. We show that strong rational synthesis is 2ExpTime-complete, thus it is not more complex than traditional synthesis or rational synthesis. Second, we study a richer specification formalism, where the objectives of the system and the agents are not Boolean but quantitative. In this setting, the objective of the system and the agents is to maximize their outcome. The quantitative setting significantly extends the scope of rational synthesis, making the game-theoretic approach much more relevant. Finally, we enrich the setting to one that allows coalitions of agents that constitute the system or the environment.     

Synthesis from component libraries

Synthesis is the automated construction of a system from its specification. In the classical temporal synthesis algorithms, it is always assumed the system is “constructed from scratch” rather than “composed” from reusable components. This, of course, rarely happens in real life. In real life, almost every non-trivial commercial system, either in hardware or in software system, relies heavily on using libraries of reusable components. Furthermore, other contexts, such as web-service orchestration, can be modeled as synthesis of a system from a library of components. In this work, we define and study the problem of LTL synthesis from libraries of reusable components. We define two notions of composition: data-flow composition, for which we prove the problem is undecidable, and control-flow composition, for which we prove the problem is 2EXPTIME-complete. As a side benefit, we derive an explicit characterization of the information needed by the synthesizer on the underlying components. This characterization can be used as a specification formalism between component providers and integrators.     

From object-process analysis to object-process design

The object-process methodology incorporates the system static-structural and dynamic-procedural aspects into a single, unified model. This unification bridges the gap that separates the static, object model from the dynamic, behavior, state, or function-oriented models found in many current object oriented methodologies. In this work we concentrate on the transition from object-process analysis to design within the development of information systems. We use a detailed case study as a running example throughout the paper to demonstrate how the structure-behavior unification, which characterizes object-process analysis, is carried on to object-process design. The case study first applies object-process analysis to perform the analysis stage. The sequence of steps that constitutes the design is then discussed and demonstrated through the case study. The design is divided into two phases: the analysis refinement phase and the implementation-dependent phase. Analysis refinement is concerned with adding details to the analysis results which are beyond the interest of the analysis itself, yet they are not related with a particular implementation. The implementation-dependent phase is concerned with code-level design, which takes place after specific implementation details, such as programming language, data organization, and user interface, have been made during the strategic design.     

Expert-driven genetic algorithms for simulating evaluation functions

In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function’s parameters for computer chess. Our results show that using an appropriate expert (or mentor), we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program that mimics the behavior of a superior expert. The resulting evaluation function of the evolved program consists of a much smaller number of parameters than the expert’s. The extended experimental results provided in this paper include a report on our successful participation in the 2008 World Computer Chess Championship. In principle, our expert-driven approach could be used in a wide range of problems for which appropriate experts are available.